3D object detection. With the increase in availability of multiple large-scale lidar datasets, there have been many recent networks proposed for 3D object detection. Here, we focus on pure lidar-based detectors, although there have been several successful multi-modal approaches [1]}, [2]}, [3]}. The seminal works PointNet [4]} and PointNet++ [5]} for the hierarchical feature extraction of pointclouds have spurred numerous deep neural networks for this task that can be broadly categorised as voxel-based methods [6]}, [7]}, [8]}, which divide the pointcloud into volumetric grids before performing feature extraction, and point-based methods [9]}, [10]} for 3D object detection, which operate directly each point in the 3D scene. In [8]}, Yan propose SECOND, a single stage, voxel-based method that utilizes 3D sparse convolutions and a Region Proposal Network (RPN) head to predict the location and category of objects in a lidar scene. Pointpillars [7]} by Lang builds on this by changing the shape of the voxels to columns that span the height of the pointcloud. PointRCNN is a two-stage network that generates 3D bounding box proposals followed by a refinement stage similar to [13]}.
